class_docstr: Abstract base layer for separable convolution.
  
  This layer performs a depthwise convolution that acts separately on
  channels, followed by a pointwise convolution that mixes channels. If
  `use_bias` is True and a bias initializer is provided, it adds a bias vector
  to the output.
  
  Args:
      rank: int, the rank of the convolution, e.g. 2 for 2D convolution.
      depth_multiplier: The number of depthwise convolution output channels
          for each input channel. The total number of depthwise convolution
          output channels will be equal to `input_channel * depth_multiplier`.
      filters: int, the dimensionality of the output space (i.e. the number
          of filters in the pointwise convolution).
      kernel_size: int or tuple/list of `rank` integers, specifying the size
          of the depthwise convolution window.
      strides: int or tuple/list of `rank` integers, specifying the stride
          length of the depthwise convolution. If only one int is specified,
          the same stride size will be used for all dimensions.
          `stride value != 1` is incompatible with `dilation_rate != 1`.
      padding: string, either `"valid"` or `"same"` (case-insensitive).
          `"valid"` means no padding. `"same"` results in padding evenly to
          the left/right or up/down of the input. When `padding="same"` and
          `strides=1`, the output has the same size as the input.
      data_format: string, either `"channels_last"` or `"channels_first"`.
          The ordering of the dimensions in the inputs. `"channels_last"`
          corresponds to inputs with shape `(batch, steps, features)`
          while `"channels_first"` corresponds to inputs with shape
          `(batch, features, steps)`. It defaults to the `image_data_format`
          value found in your Keras config file at `~/.keras/keras.json`.
          If you never set it, then it will be `"channels_last"`.
      dilation_rate: int or tuple/list of `rank` integers, specifying the
          dilation rate to use for dilated convolution. If only one int is
          specified, the same dilation rate will be used for all dimensions.
      activation: Activation function. If `None`, no activation is applied.
      use_bias: bool, if `True`, bias will be added to the output.
      depthwise_initializer: An initializer for the depthwise convolution
          kernel. If None, then the default initializer (`"glorot_uniform"`)
          will be used.
      pointwise_initializer: An initializer for the pointwise convolution
          kernel. If None, then the default initializer (`"glorot_uniform"`)
          will be used.
      bias_initializer: An initializer for the bias vector. If None, the
          default initializer ('"zeros"') will be used.
      depthwise_regularizer: Optional regularizer for the depthwise
          convolution kernel.
      pointwise_regularizer: Optional regularizer for the pointwise
          convolution kernel.
      bias_regularizer: Optional regularizer for the bias vector.
      activity_regularizer: Optional regularizer function for the output.
      depthwise_constraint: Optional projection function to be applied to the
          depthwise kernel after being updated by an `Optimizer` (e.g. used
          for norm constraints or value constraints for layer weights). The
          function must take as input the unprojected variable and must return
          the projected variable (which must have the same shape).
      pointwise_constraint: Optional projection function to be applied to the
          pointwise kernel after being updated by an `Optimizer`.
      bias_constraint: Optional projection function to be applied to the
          bias after being updated by an `Optimizer`.
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code: def get_config(self):
      config = super().get_config()
      config.update(
          {
              "depth_multiplier": self.depth_multiplier,
              "filters": self.filters,
              "kernel_size": self.kernel_size,
              "strides": self.strides,
              "padding": self.padding,
              "data_format": self.data_format,
              "dilation_rate": self.dilation_rate,
              "activation": activations.serialize(self.activation),
              "use_bias": self.use_bias,
              "depthwise_initializer": initializers.serialize(
                  self.depthwise_initializer
              ),
              "pointwise_initializer": initializers.serialize(
                  self.pointwise_initializer
              ),
              "bias_initializer": initializers.serialize(
                  self.bias_initializer
              ),
              "depthwise_regularizer": regularizers.serialize(
                  self.depthwise_regularizer
              ),
              "pointwise_regularizer": regularizers.serialize(
                  self.pointwise_regularizer
              ),
              "bias_regularizer": regularizers.serialize(
                  self.bias_regularizer
              ),
              "activity_regularizer": regularizers.serialize(
                  self.activity_regularizer
              ),
              "depthwise_constraint": constraints.serialize(
                  self.depthwise_constraint
              ),
              "pointwise_constraint": constraints.serialize(
                  self.pointwise_constraint
              ),
              "bias_constraint": constraints.serialize(
                  self.bias_constraint
              ),
          }
      )
      return config
  
file_path: keras/keras/layers/convolutional/base_separable_conv.py
imports: from keras import activations
  from keras import constraints
  from keras import initializers
  from keras import ops
  from keras import regularizers
  from keras.backend import standardize_data_format
  from keras.layers.input_spec import InputSpec
  from keras.layers.layer import Layer
  from keras.ops.operation_utils import compute_conv_output_shape
  from keras.utils.argument_validation import standardize_padding
  from keras.utils.argument_validation import standardize_tuple
label_desc: Use image node_id 5 for calling the BaseSeparableConv obj's underlying member method code with example usage: obj.get_config() and returns: config